Abstract
Visible-infrared person re-identification (VIReID) provides a solution for ReID tasks in 24-hour scenarios; however, significant challenges persist in achieving satisfactory performance due to the substantial discrepancies between visible (VIS) and infrared (IR) modalities. Existing methods inadequately leverage information from different modalities, primarily focusing on digging distinguishing features from modality-shared information while neglecting modality-specific details. To fully utilize differentiated minutiae, we propose a Base-Detail Feature Learning Framework (BDLF) that enhances the learning of both base and detail knowledge, thereby capitalizing on both modality-shared and modality-specific information. Specifically, the proposed BDLF mines detail and base features through a lossless detail feature extraction module and a complementary base embedding generation mechanism, respectively, supported by a novel correlation restriction method that ensures the features gained by BDLF enrich both detail and base knowledge across VIS and IR features. Comprehensive experiments conducted on the SYSU-MM01, RegDB, and LLCM datasets validate the effectiveness of BDLF.
Abstract (translated)
可见光-红外行人重识别（VIReID）为全天候场景下的重识别任务提供了一种解决方案；然而，由于可见光（VIS）和红外线（IR）模态之间的显著差异，要达到令人满意的性能仍然面临着重大挑战。现有的方法未能充分利用不同模态的信息，主要集中在挖掘模态共享信息中的区分特征，而忽略了模态特有的细节。 为了充分利用差异化的小细节，我们提出了一种基元-细节特征学习框架（BDLF），该框架增强了基础和详细知识的学习能力，并且能够同时利用模态共享和特定于某个模态的信息。具体来说，提出的BDLF通过无损的细节特征提取模块挖掘细节特征，并使用互补的基础嵌入生成机制来获取基础特征，这些都得到了一种新颖的相关性限制方法的支持，该方法确保了由BDLF获得的特征能够丰富跨VIS和IR特征的细节与基础知识。 在SYSU-MM01、RegDB 和LLCM数据集上进行的全面实验验证了BDLF的有效性。
URL
https://arxiv.org/abs/2505.03286
